Lozano Ariel, Escribano Bruno, Akhmatskaya Elena, Carrasco Javier
Basque Center for Applied Mathematics, Alameda de Mazarredo 14, (48009) Bilbao, Bizkaia, Spain.
Phys Chem Chem Phys. 2017 Apr 12;19(15):10133-10139. doi: 10.1039/c7cp00284j.
The discovery of computationally driven materials requires efficient and accurate methods. Density functional theory (DFT) meets these two requirements for many classes of materials. However, DFT-based methods have limitations. One significant shortcoming is the inadequate treatment of weak van der Waals (vdW) interactions, which are crucial for layered materials. Here we assess the performance of various vdW-inclusive DFT approaches for predicting the structure and voltage of layered electroactive materials for Li-ion batteries, considering a set of 20 different compounds. We find that the so-called optB86b-vdW density functional improves the agreement with the experimental data, closely followed by the latest generation of dispersion correction methods. These approaches yield average relative errors for the structural parameters smaller than 3%. The average deviations for redox potentials are below 0.15 V. Looking ahead, this study identifies accurate methods for Li-ion vdW bound systems, providing enhanced predictive power to DFT-assisted design for developing new types of electroactive materials in general.
通过计算驱动发现材料需要高效且准确的方法。密度泛函理论（DFT）对许多类材料满足这两个要求。然而，基于DFT的方法存在局限性。一个显著缺点是对弱范德华（vdW）相互作用处理不足，而这种相互作用对层状材料至关重要。在此，我们考虑一组20种不同化合物，评估各种包含vdW的DFT方法在预测锂离子电池层状电活性材料的结构和电压方面的性能。我们发现所谓的optB86b-vdW密度泛函改善了与实验数据的一致性，紧随其后的是最新一代的色散校正方法。这些方法给出的结构参数平均相对误差小于3%。氧化还原电位的平均偏差低于0.15 V。展望未来，本研究确定了用于锂离子vdW键合体系的准确方法，总体上为开发新型电活性材料的DFT辅助设计提供了更强的预测能力。
